Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks.

翻译：自然语言处理（NLP）的最新进展在很大程度上归功于BERT和RoBERTa等预训练语言模型的出现。尽管这些模型在通用数据集上表现出色，但在医学等专业领域却可能面临挑战，因为这些领域普遍存在独特的专业术语、领域特异性缩写以及多变的文档结构。本文探讨了通过领域专用数据的持续预训练来适配这些模型以适应特定领域需求的策略。我们利用来自翻译后的公开英语医学数据的24亿个token和30亿个德语临床数据token，预训练了多个德语医学语言模型。所得模型在多项德语下游任务上进行了评估，包括命名实体识别（NER）、多标签分类和抽取式问答。结果表明，通过临床数据和翻译数据增强预训练的模型在医学语境中通常优于通用领域模型。我们得出结论，持续预训练能够匹配甚至超越从头训练的临床模型的性能。此外，在临床数据上进行预训练或利用翻译文本已被证明是医学NLP任务中领域适配的可靠方法。